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SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation
TLDR
We present SimLex-999, a gold standard resource for evaluating distributional semantic models that improves on existing resources in several important ways. Expand
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SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity
TLDR
We introduce SimVerb-3500, an evaluation resource that provides human ratings for the similarity of 3,500 verb pairs from the USF free-association database that is unprecedented in both size and coverage. Expand
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The Hitchhiker's Guide to Testing Statistical Significance in Natural Language Processing
TLDR
Statistical significance testing is a standard statistical tool designed to ensure that experimental results are not coincidental. Expand
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Modeling the Detection of Textual Cyberbullying
TLDR
The scourge of textual cyberbullying has assumed alarming proportions with an ever-increasing number of adolescents admitting to having dealt with it either as a victim or as a bystander. Expand
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Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
TLDR
We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources to tune word vector spaces using linguistic information that is difficult to capture with conventional distributional training. Expand
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Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
TLDR
We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources. Expand
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Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction
TLDR
We present a novel word level vector representation based on symmetric patterns (SPs). Expand
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Separated by an Un-common Language: Towards Judgment Language Informed Vector Space Modeling
TLDR
In this paper we translate two prominent evaluation sets, wordsim353 (association) and SimLex999 (similarity), from English to Italian, German and Russian and collect scores for each dataset from crowdworkers fluent in its language. Expand
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Multi-Task Active Learning for Linguistic Annotations
TLDR
In the multi-task active learning (MTAL) paradigm, we select examples for several annotation tasks rather than for a single one as usually done in the context of AL. Expand
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Neural Structural Correspondence Learning for Domain Adaptation
TLDR
We introduce a neural network model that marries together ideas from two prominent strands of research on domain adaptation through representation learning: structural correspondence learning (SCL, (Blitzer et al., 2006)) and autoencoder neural networks. Expand
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